The organization of eco-industrial parks and their sustainable practices

Eco-Industrial Parks (EIPs) are defined as a community of firms located in the same area and linked in a network of collaborative relationships mainly aimed at enhancing sustainability. A number of EIPs have recently spread in both developed and developing countries through diverse formation processes, resulting in different configurations. The topic has received a growing attention by the literature, even though to our knowledge the available studies lack to characterize the EIPs' organizational models and analyse how models reflect on the EIP's sustainability. The aim of this paper is to fill this gap, proposing a framework that characterizes EIPs along two dimensions related to organization and sustainability, which are further described through specific variables. We apply the framework on 28 EIPs and conduct cluster analysis to group them according to the organizational dimension. We then identify different organizational models of EIPs and discuss the possible linkages between such models and the adopted sustainability practices. The research findings have practical implications concerning policies and strategies to enhance ElPs sustainability. (C) 2017 Elsevier Ltd. All rights reserved

Well-Being and Sustainability in Crisis Areas: The Case of Taranto

Unresilient and fragile regions need methods and data able to make policy-makers acknowledge the specific criticalities by which they are affected, so as to build effective development strategies and policies. This research explores whether and to what extent well-being and sustainability measurement frameworks are able to recognize crisis areas. We identified Taranto (Italy), declared as both a National Priority Contaminated Site and a Complex Industrial Crisis area, as a paradigmatic and extreme case of crisis areas and adopted the single case approach to address our research question. After reviewing several frameworks able to measure well-being at local level, we focused on Benessere Equo e Sostenibile dei Territori (Equitable and Sustainable Territorial Well-being, BESdT). We used two aggregate indexes to analyze data, namely the Adjusted Mazziotta-Pareto Index and the Adjusted Differences Mean Index. The study shows that, although BESdT does detect some criticalities of the examined area, it seems not able to adequately frame the multifaceted crisis that affects the area of Taranto. Even in presence of a full-blown crisis, the problematic situation does not always reflect into lower territorial performance, neither at the level of single indicators nor at the level of entire domains. Such discrepancy appears to be particularly evident within the economic domain. The paper ends with a discussion on the research and policy implications and some proposals for further research

Multi-Time-Scale Features for Accurate Respiratory Sound Classification

The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms. This need was already clear to the scientific community, which launched an international challenge in 2017 at the International Conference on Biomedical Health Informatics (ICBHI) for the implementation of accurate algorithms for the classification of respiratory sound. In this work, we present a framework for respiratory sound classification based on two different kinds of features: (i) short-term features which summarize sound properties on a time scale of tenths of a second and (ii) long-term features which assess sounds properties on a time scale of seconds. Using the publicly available dataset provided by ICBHI, we cross-validated the classification performance of a neural network model over 6895 respiratory cycles and 126 subjects. The proposed model reached an accuracy of 85%±3% and an precision of 80%±8%, which compare well with the body of literature. The robustness of the predictions was assessed by comparison with state-of-the-art machine learning tools, such as the support vector machine, Random Forest and deep neural networks. The model presented here is therefore suitable for large-scale applications and for adoption in clinical practice. Finally, an interesting observation is that both short-term and long-term features are necessary for accurate classification, which could be the subject of future studies related to its clinical interpretation

Economic Interplay Forecasting Business Success

A startup ecosystem is a dynamic environment in which several actors, such as investors, venture capitalists, angels, and facilitators, are the protagonists of a complex interplay. Most of these interactions involve the flow of capital whose size and direction help to map the intricate system of relationships. This quantity is also considered a good proxy of economic success. Given the complexity of such systems, it would be more desirable to supplement this information with other informative features, and a natural choice is to adopt mathematical measures. In this work, we will specifically consider network centrality measures, borrowed by network theory. In particular, using the largest publicly available dataset for startups, the Crunchbase dataset, we show how centrality measures highlight the importance of particular players, such as angels and accelerators, whose role could be underestimated by focusing on collected funds only. We also provide a quantitative criterion to establish which firms should be considered strategic and rank them. Finally, as funding is a widespread measure for success in economic settings, we investigate to which extent this measure is in agreement with network metrics; the model accurately forecasts which firms will receive the highest funding in future years

Predicting brain age with complex networks: From adolescence to adulthood.

In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7-64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson's correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging

Territorial Development as an Innovation Driver: A Complex Network Approach

Rankings are a well-established tool to evaluate the performance of actors in different sectors of the economy, and their use is increasing even in the context of the startup ecosystem, both on a regional and on a global scale. Although rankings meet the demand for measurability and comparability, they often provide an oversimplified picture of the status quo, which, in particular, overlooks the variability of the socio-economic conditions in which the quantified results are achieved. In this paper, we describe an approach based on constructing a network of world countries, in which links are determined by mutual similarity in terms of development indicators. Through the instrument of community detection, we perform an unsupervised partition of the considered set of countries, aimed at interpreting their performance in the StartupBlink rankings. We consider both the global ranking and the specific ones (quality, quantity, business). After verifying if community membership is predictive of the success of a country in the considered ranking, we rate country performances in terms of the expectation based on community peers. We are thus able to identify cases in which performance is better than expected, providing a benchmark for countries in similar conditions, and cases in which performance is below the expectation, highlighting the need to strengthen the innovation ecosystem

Best Practices in Knowledge Transfer: Insights from Top Universities

The impact of knowledge transfer induced by universities on economy, society, and culture is widely acknowledged; nevertheless, this aspect is often neglected by university rankings. Here, we considered three of the most popular global university rankings and specific knowledge transfer indicators by U-multirank, a European ranking system launched by the European Commission, in order to answer to the following research question: how do the world top universities, evaluated according to global university rankings, perform from a knowledge transfer point of view? To this aim, the top universities have been compared with the others through the calculation of a Global Performance Indicator in Knowledge Transfer (GPI KT), a hierarchical clustering, and an outlier analysis. The results show that the universities best rated by global rankings do not always perform as well from knowledge transfer point of view. By combining the obtained results, it is possible to state that only 5 universities (Berkeley, Stanford, MIT, Harvard, CALTEC), among the top in the world, exhibit a high-level performance in knowledge transfer activities. For a better understanding of the success factors and best practices in knowledge transfer, a brief description of the 5 cited universities, in terms of organization of technology transfer service, relationship with business, entrepreneurship programs, and, more generally, third mission activities, is provided. A joint reading of the results suggests that the most popular global university rankings probably fail to effectively photograph third mission activities because they can manifest in a variety of forms, due to the intrinsic and intangible nature of third mission variables, which are difficult to quantify with simple and few indicators

Accurate Evaluation of Feature Contributions for Sentinel Lymph Node Status Classification in Breast Cancer

The current guidelines recommend the sentinel lymph node biopsy to evaluate the lymph node involvement for breast cancer patients with clinically negative lymph nodes on clinical or radiological examination. Machine learning (ML) models have significantly improved the prediction of lymph nodes status based on clinical features, thus avoiding expensive, time-consuming and invasive procedures. However, the classification of sentinel lymph node status represents a typical example of an unbalanced classification problem. In this work, we developed a ML framework to explore the effects of unbalanced populations on the performance and stability of feature ranking for sentinel lymph node status classification in breast cancer. Our results indicate state-of-the-art AUC (Area under the Receiver Operating Characteristic curve) values on a hold-out set (67%) while providing particularly stable features related to tumor size, histological subtype and estrogen receptor expression, which should therefore be considered as potential biomarkers

Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age

Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, evaluating both the intensity and the uniformity of connections, to predict subjects' ages. Our model reaches high accuracies which compare favorably with state-of-the-art approaches. We observe that the complex relationships involved in this brain description cannot be accurately modeled with standard machine learning approaches, such as Ridge and Lasso regression, Random Forest, and Support Vector Machines, instead a deep neural network has to be used